Introduction¶

This notebook aims to identify what, if any, correlations exists between the number of meteorite landings and the size and location of the landing.

In [1]:
import pandas as pd
import numpy as np
import geopandas as gpd
from shapely.geometry import Point
import folium
import plotly.express as px
import plotly.graph_objects as go
from IPython.display import display
%matplotlib inline

# Load the dataset
df = pd.read_csv('/Users/heatheradler/Documents/GitHub/Springboard/Springboard_Projects/Data Storytelling/Meteorite_Landings.csv')

Data Exploration¶

We start by exploring the dataset to understand its structure and clean it if necessary.

In [2]:
df.rename({"mass (g)": "mass_in_grams", "reclat": "latitude", "reclong": "longitude"}, axis=1, inplace=True)

Data Cleaning¶

Next, we convert remove missing values in column 'mass (g)' and repalce remaining null values so that there are no longer any missing vlaues.

In [3]:
df_1 = df.dropna(subset=['mass_in_grams','latitude', 'longitude'])

df_1.fillna(0, inplace=True)

/var/folders/9q/j_dp7nx93lbc_t5zbbshxpch0000gn/T/ipykernel_82899/497208440.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_1.fillna(0, inplace=True)
In [4]:
#Create Point geometries
geometry = [Point(lon, lat) for lon, lat in zip(df_1['longitude'], df_1['latitude'])]
gdf_points = gpd.GeoDataFrame(df_1, geometry=geometry, crs='EPSG:4326')

Data Analysis¶

We'll perform various analyses to explore the dataset more deeply.

In [5]:
# Create Point geometries
geometry = [Point(lon, lat) for lon, lat in zip(df_1['longitude'], df_1['latitude'])]
gdf_points = gpd.GeoDataFrame(df_1, geometry=geometry, crs='EPSG:4326')

# Load world countries shapefile
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))

# Perform spatial join
joined = gpd.sjoin(gdf_points, world[['geometry', 'iso_a3']], op='within')
country_names = joined['iso_a3']

df_1['country'] = country_names

merged_df = df_1.merge(world[['iso_a3', 'name']], how='left', left_on='country', right_on='iso_a3')
merged_df = merged_df.drop(columns=['iso_a3']).rename(columns={'name_y': 'country_name'})

/var/folders/9q/j_dp7nx93lbc_t5zbbshxpch0000gn/T/ipykernel_82899/294049908.py:6: FutureWarning: The geopandas.dataset module is deprecated and will be removed in GeoPandas 1.0. You can get the original 'naturalearth_lowres' data from https://www.naturalearthdata.com/downloads/110m-cultural-vectors/.
  world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
/Users/heatheradler/anaconda3/lib/python3.11/site-packages/IPython/core/interactiveshell.py:3466: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.
  if await self.run_code(code, result, async_=asy):
/var/folders/9q/j_dp7nx93lbc_t5zbbshxpch0000gn/T/ipykernel_82899/294049908.py:12: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_1['country'] = country_names
In [6]:
# Create folium map
df1_filtered = merged_df[['country_name', 'latitude', 'longitude', 'mass_in_grams']].dropna(subset=['country_name', 'latitude', 'longitude', 'mass_in_grams']).copy()
f = folium.Figure(width=1000, height=500)
m = folium.Map(location=df1_filtered[['latitude', 'longitude']].mean().values.tolist()).add_to(f)

for index, row in df1_filtered.iterrows():
    folium.CircleMarker(
        location=[row['latitude'], row['longitude']],
        radius=1,
        color='blue',
        fill=True,
        fill_color='blue',
        fill_opacity=0.6,
        popup=f'Name: {row["country_name"]}\nMass: {row["mass_in_grams"]}g'
    ).add_to(m)

m.fit_bounds([df1_filtered[['latitude', 'longitude']].min().values.tolist(), df1_filtered[['latitude', 'longitude']].max().values.tolist()])

Questions to Answer with Analytics:¶

1) What are the top 10 countries with the highest number of recorded meteorite landings?¶

In [7]:
df1_country_count = merged_df.groupby(['country_name'])[['country']].count().rename(columns={
    "country": "meteorite_landings"
    }).sort_values(by=['meteorite_landings'], ascending=False).reset_index()
df1_country_count.head(10)
Out[7]:
country_name meteorite_landings
0 Antarctica 22097
1 Oman 3093
2 United States of America 1652
3 Libya 1474
4 Australia 632
5 Algeria 624
6 Chile 359
7 Kenya 227
8 Morocco 218
9 India 131
In [8]:
fig = px.bar(
    df1_country_count.head(10).sort_values(by=['meteorite_landings'], ascending=True),
    x='meteorite_landings',
    y='country_name',
    orientation='h',  # horizontal bar chart
    title="Meteorite Landings by Country",
    labels={'meteorite_landings': 'Meteorite Landings Count', 'country_name': 'Country'}
)

fig.show()

2) What are the top 10 meteorite landings worldwide based on their mass?¶

In [9]:
df1_by_mass = merged_df.sort_values(by=['mass_in_grams'], ascending=False).head(10).copy()
df1_by_mass['mass_in_grams'] = df1_by_mass['mass_in_grams'].astype(int)

fig = px.scatter(df1_by_mass, y="country_name", x="year", color="country", size='mass_in_grams', width=1000, height=400)
fig.update_layout(
    title="<b>Scatter Plot of Top 10 Heaviest Meteorite Landings</b>",
    xaxis_title="<b>Year of Meteorite Landings</b>",
    yaxis_title="<b>Country</b>",
    legend_title="Country"
)
fig.show()

Observations:¶

(1) The heaviest meteor landing was found in Nambia (1920).
(2) Out of the top 10 heaviest meteorite landings, Namibia and Mexico recorded 2 each.
(3) Out of the top 10 heaviest meteorite landings the oldest recorded was in Argentina (1575).

3) What is the cumulative count of meteorite landings per year over time¶

In [10]:
fig = px.bar(merged_df['year'].value_counts().sort_index().reset_index(), x='year', y='count', width=1000, height=400)
fig.update_layout(
    title="<b>Bar Plot of Yearly Meteorite Landings</b>",
    xaxis_range=[ 1950, 2023 ],
    xaxis_title="<b>Year of Meteorite Landings</b>",
    yaxis_title="<b>Total Meteorite Landings</b>"
    )
fig.update_traces(marker_color='green')
fig.show()

Observations:¶

The count of meteorite landings reached its peak in 1979. Since then, the meteorite landing count has fluctuated with highs in 1988 and 1998.

4) What is the distribution of meteorite landings based on their class type?¶

In [11]:
df1_class = merged_df['recclass'].value_counts().reset_index()

total_count = df1_class['count'].sum()
df1_class['percentage'] = round((df1_class['count'] / total_count) * 100, 0)

threshold_percentage = 5

filtered_df = df1_class[df1_class['percentage'] >= threshold_percentage]

fig = px.pie(filtered_df, values='percentage', names='recclass', width=600, height=600)
fig.update_layout(title="<b>Meteorite Landings By Class Type</b>", )
fig.update_traces(textposition='inside', texttemplate='%{label}<br>%{value}%')    
fig.show()

5) What are the top 10 meteorite classes based on their average mass within the class and their class count?¶

In [12]:
df1_class = merged_df.groupby('recclass')['mass_in_grams'].agg(['mean', 'count']).reset_index().rename({'mean': 'mass_in_grams'}, axis='columns')
df1_class['mass_in_kg'] = round(df1_class['mass_in_grams'] * 0.001, 1)
df1_class = df1_class.sort_values(by='count', ascending=False).head(10)

bar_trace = go.Bar(x=df1_class.head(10)['recclass'], y=df1_class.head(10)['mass_in_kg'], name='Average Mass of Meteorite (kg)')
scatter_trace = go.Scatter(
    x=df1_class.head(10)['recclass'], y=df1_class.head(10)['count'], mode='markers+lines', name='Meteorite Class Count', yaxis='y2'
)

fig = go.Figure(data=[bar_trace, scatter_trace])
fig.update_layout(
    xaxis=dict(title='<b>Meteorite Class</b>'),
    yaxis=dict(title='<b>Average Mass of Meteorite (kg)</b>'),
    yaxis2=dict(title='<b>Meteorite Class Count</b>', overlaying='y', side='right'),
    title="<b>Bar Plot of Average Meteorite Mass (in kg) by Class, Sorted by Class Count</b>",
    legend=dict(orientation='h', yanchor='top', y=1.15, xanchor='right', x=1),
    width=1000,
    height=400
)
fig.show()
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